Line outage identification in distribution grids is essential for sustainable grid operation. In this work, we propose a practical yet robust detection approach that utilizes only readily available voltage magnitudes, eliminating the need for costly phase angles or power flow data. Given the sensor data, many existing detection methods based on change-point detection require prior knowledge of outage patterns, which are unknown for real-world outage scenarios. To remove this impractical requirement, we propose a data-driven method to learn the parameters of the post-outage distribution through gradient descent. However, directly using gradient descent presents feasibility issues. To address this, we modify our approach by adding a Bregman divergence constraint to control the trajectory of the parameter updates, which eliminates the feasibility problems. As timely operation is the key nowadays, we prove that the optimal parameters can be learned with convergence guarantees via leveraging the statistical and physical properties of voltage data. We evaluate our approach using many representative distribution grids and real load profiles with 17 outage configurations. The results show that we can detect and localize the outage in a timely manner with only voltage magnitudes and without assuming a prior knowledge of outage patterns.

翻译：在配电网中进行线路故障辨识对于电网的可持续运行至关重要。本文提出一种实用且鲁棒的检测方法，该方法仅利用易于获取的电压幅值信息，无需昂贵的相角或潮流数据。在给定传感器数据的情况下，许多基于变点检测的现有方法需要预先了解故障模式，而实际故障场景中这些模式通常是未知的。为消除这一不切实际的要求，我们提出一种数据驱动方法，通过梯度下降学习故障后分布的参数。然而，直接使用梯度下降存在可行性问题。为此，我们通过添加Bregman散度约束来改进方法，以控制参数更新的轨迹，从而消除可行性问题。鉴于及时操作是当前的关键，我们利用电压数据的统计与物理特性，证明了最优参数可在收敛保证下学习得到。我们使用多种代表性配电网和17种故障配置的真实负荷曲线评估了所提方法。结果表明，该方法仅依赖电压幅值即可在无需预先假设故障模式的情况下及时检测并定位故障。